Artificial intelligence, machine learning and the evolution of healthcare

Abstract

vol. 7, No. 3, MaRch 2018 223 First proposed by Professor John Mccarthy at Dartmouth college in the summer of 1956,1 artificial Intelligence (aI) – human intelligence exhibited by machines – has occupied the lexicon of successive generations of computer scientists, science fiction fans, and medical researchers. The aim of countless careers has been to build intelligent machines that can interpret the world as humans do, understand language, and learn from realworld examples. In the early part of this century, two events coincided that transformed the field of aI. The advent of widely available Graphic Processing Units (GPUs) meant that parallel processing was faster, cheaper, and more powerful. at the same time, the era of ‘Big Data’ – images, text, bioinformatics, medical records, and financial transactions, among others – was moving firmly into the mainstream, along with almost limitless data storage. These factors led to a dramatic resurgence in interest in aI in both academic circles and industries outside traditional computer science. once again, aI occupies the zeitgeist, and is poised to transform medicine at a basic science, clinical, healthcare management, and financial level. Terminology surrounding these technologies continues to evolve and can be a source of confusion for non-computer scientists. aI is broadly classified as: general aI, machines that replicate human thought, emotion, and reason (and remain, for now, in the realm of science fiction); and narrow aI, technologies that can perform specific tasks as well as, or better than, humans. Machine learning (Ml) is the study of computer algorithms that can learn complex relationships or patterns from empirical data and make accurate decisions.2 Rather than coding specific sets of instructions to accomplish a task, the machine is ‘trained’ using large amounts of data and algorithms that confer it the ability to learn how to perform the task. Unlike normal algorithms, it is the data that ‘tells’ the machine what the ‘good answer’ is, and learning occurs without explicit programming. Ml problems can be classified as supervised learning or unsupervised learning.3 In a supervised machine learning algorithm, such as face recognition, the machine is shown several examples of ‘face’ or ‘non-face’ and the algorithm learns to predict whether an unseen image is a face or not. In unsupervised learning, the images shown to the machine are not labelled as ‘face’ or ‘non-face’. artificial Neural Networks (aNN)4 are one group of algorithms used for machine learning. While aNNs have existed for over 60 years, they fell out of favour during the 1990s and 2000s. In the last half-decade, aNNs have had a resurgence under a new name: deep artificial networks (or ‘Deep learning’). aNNs are uniquely poised to take full advantage of the computational boost offered by GPUs, allowing them to crunch through data sets of enormous sizes. These range from computer vision tasks, such as image classification, object detection, face recognition, and optical character recognition (ocR), to natural language processing and even gameplaying problems (from mastering simple atari games to the recent alphaGo victory against human grandmasters).5 aNNs work by constructing layers upon layers of simple processing units (often referred to as ‘neurons’), interconnected via many differentially weighted connections. aNNs are ‘trained’ by using backpropagation algorithms, essentially telling the machine how to alter the internal parameters that are used to compute the representation in each layer from the representation in the previous Artificial intelligence, machine learning and the evolution of healthcare